MACHINE LEARNING FOR INTRUSION DETECTION: TRENDS,CHALLENGES AND FUTURE DIRECTIONS

Networks and information systems must be protected from cyberattacks by intrusion detection systems. Traditional rule- based intrusion detection systems are becoming less and less effective against complex and dynamic threats. Consequently, machine learning (ML) approaches are being used to create more intelligent, flexible and instantaneous threat detection systems. The Machine learning approaches utilised in IDS are supervised, unsupervised, ensemble, deep learning and federated learning models which is thoroughly reviewed in this work. Their benefits, drawbacks and practical uses are highlighted. This study also covers performance evaluation measures, datasets and new developments including edge computing solutions, privacy- preserving intrusion detection systems and Explainable AI (XAI). The study offers possible remedies while shedding light on problems related to feature selection, class imbalance, scalability and false positives. This research aims to provide researchers and practitioners with a better understanding of recent advancements in IDS models.